Volunteer Summary

CONSORT Flow Diagram

Overall status

Characteristic

Overall1

Control1

Treatment1

time_point

1st

91

51

40

2nd

67

34

33

1n

Demographic information

Characteristic

N

Overall, N = 911

control, N = 511

treatment, N = 401

p-value2

age

90

40.53 ± 17.78 (21 - 148)

41.46 ± 19.78 (22 - 148)

39.36 ± 15.09 (21 - 70)

0.580

Unknown

1

1

0

gender

90

0.232

female

64 (71%)

33 (66%)

31 (78%)

male

26 (29%)

17 (34%)

9 (22%)

Unknown

1

1

0

occupation

90

0.754

civil

3 (3.3%)

2 (4.0%)

1 (2.5%)

clerk

17 (19%)

8 (16%)

9 (22%)

homemaker

8 (8.9%)

3 (6.0%)

5 (12%)

manager

11 (12%)

7 (14%)

4 (10%)

other

10 (11%)

4 (8.0%)

6 (15%)

professional

13 (14%)

10 (20%)

3 (7.5%)

retired

4 (4.4%)

2 (4.0%)

2 (5.0%)

service

4 (4.4%)

2 (4.0%)

2 (5.0%)

student

18 (20%)

11 (22%)

7 (18%)

unemploy

2 (2.2%)

1 (2.0%)

1 (2.5%)

Unknown

1

1

0

working_status

91

59 (65%)

34 (67%)

25 (62%)

0.679

marital

90

0.715

divorced

3 (3.3%)

1 (2.0%)

2 (5.0%)

married

25 (28%)

15 (30%)

10 (25%)

single

61 (68%)

33 (66%)

28 (70%)

widowed

1 (1.1%)

1 (2.0%)

0 (0%)

Unknown

1

1

0

marital_r

90

0.866

married

25 (28%)

15 (30%)

10 (25%)

other

4 (4.4%)

2 (4.0%)

2 (5.0%)

single

61 (68%)

33 (66%)

28 (70%)

Unknown

1

1

0

education

90

0.017

primary

0 (0%)

0 (0%)

0 (0%)

secondary

11 (12%)

2 (4.0%)

9 (22%)

post-secondary

15 (17%)

11 (22%)

4 (10%)

university

64 (71%)

37 (74%)

27 (68%)

Unknown

1

1

0

university_edu

90

64 (71%)

37 (74%)

27 (68%)

0.499

Unknown

1

1

0

family_income

90

0.335

0_10000

11 (12%)

5 (10%)

6 (15%)

10001_20000

19 (21%)

7 (14%)

12 (30%)

20001_30000

14 (16%)

9 (18%)

5 (12%)

30001_40000

13 (14%)

8 (16%)

5 (12%)

40000_above

33 (37%)

21 (42%)

12 (30%)

Unknown

1

1

0

high_income

91

46 (51%)

29 (57%)

17 (42%)

0.174

religion

90

0.567

buddhism

5 (5.6%)

4 (8.0%)

1 (2.5%)

catholic

5 (5.6%)

2 (4.0%)

3 (7.5%)

christianity

33 (37%)

19 (38%)

14 (35%)

nil

45 (50%)

25 (50%)

20 (50%)

other

1 (1.1%)

0 (0%)

1 (2.5%)

taoism

1 (1.1%)

0 (0%)

1 (2.5%)

Unknown

1

1

0

religion_r

90

>0.999

christianity

38 (42%)

21 (42%)

17 (42%)

nil

45 (50%)

25 (50%)

20 (50%)

other

7 (7.8%)

4 (8.0%)

3 (7.5%)

Unknown

1

1

0

source

90

0.023

bokss

38 (42%)

17 (34%)

21 (52%)

facebook

12 (13%)

10 (20%)

2 (5.0%)

instagram

6 (6.7%)

6 (12%)

0 (0%)

other

17 (19%)

8 (16%)

9 (22%)

refresh

17 (19%)

9 (18%)

8 (20%)

Unknown

1

1

0

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Characteristic

N

Overall, N = 911

control, N = 511

treatment, N = 401

p-value2

sets

91

19.20 ± 3.04 (0 - 25)

18.67 ± 3.40 (0 - 24)

19.88 ± 2.40 (15 - 25)

0.060

setv

91

11.11 ± 2.05 (0 - 15)

10.86 ± 2.24 (0 - 15)

11.43 ± 1.75 (8 - 15)

0.195

maks

90

44.86 ± 3.84 (36 - 57)

44.38 ± 3.56 (36 - 52)

45.45 ± 4.14 (38 - 57)

0.191

Unknown

1

1

0

ibs

91

15.49 ± 2.69 (0 - 20)

15.33 ± 2.97 (0 - 20)

15.70 ± 2.31 (9 - 20)

0.522

ers_e

91

12.11 ± 1.93 (0 - 15)

11.98 ± 2.28 (0 - 15)

12.28 ± 1.40 (9 - 15)

0.474

ers_r

91

11.20 ± 1.90 (0 - 15)

10.98 ± 2.08 (0 - 14)

11.47 ± 1.63 (8 - 15)

0.221

pss_pa

91

44.57 ± 6.55 (0 - 54)

43.82 ± 7.65 (0 - 54)

45.52 ± 4.74 (31 - 54)

0.221

pss_ps

91

25.21 ± 7.67 (0 - 42)

25.76 ± 8.19 (0 - 42)

24.50 ± 7.00 (12 - 41)

0.438

pss

91

42.95 ± 11.90 (0 - 72)

43.71 ± 12.73 (0 - 72)

41.98 ± 10.83 (21 - 67)

0.494

rki_responsible

91

21.00 ± 4.57 (0 - 29)

20.45 ± 5.18 (0 - 29)

21.70 ± 3.60 (14 - 28)

0.198

rki_nonlinear

91

13.25 ± 3.06 (0 - 22)

12.92 ± 3.14 (0 - 20)

13.68 ± 2.95 (8 - 22)

0.246

rki_peer

91

20.21 ± 3.05 (0 - 25)

20.08 ± 3.59 (0 - 25)

20.38 ± 2.20 (16 - 25)

0.647

rki_expect

91

4.63 ± 1.17 (0 - 8)

4.39 ± 1.25 (0 - 8)

4.92 ± 1.00 (3 - 7)

0.030

rki

91

59.09 ± 8.56 (0 - 80)

57.84 ± 10.17 (0 - 76)

60.67 ± 5.64 (50 - 80)

0.118

raq_possible

91

15.41 ± 2.44 (0 - 20)

15.31 ± 2.89 (0 - 20)

15.53 ± 1.74 (12 - 20)

0.684

raq_difficulty

91

12.20 ± 1.91 (0 - 15)

12.22 ± 2.24 (0 - 15)

12.18 ± 1.41 (9 - 15)

0.920

raq

91

27.60 ± 4.15 (0 - 35)

27.53 ± 4.97 (0 - 35)

27.70 ± 2.84 (21 - 35)

0.847

who

91

14.88 ± 4.60 (0 - 25)

14.76 ± 4.65 (0 - 25)

15.03 ± 4.59 (6 - 25)

0.791

phq

91

3.36 ± 3.64 (0 - 18)

3.29 ± 3.44 (0 - 14)

3.45 ± 3.92 (0 - 18)

0.841

gad

91

2.87 ± 3.02 (0 - 12)

2.90 ± 2.97 (0 - 12)

2.83 ± 3.11 (0 - 12)

0.905

nb_pcs

90

51.16 ± 7.74 (25 - 63)

51.92 ± 7.51 (25 - 63)

50.20 ± 8.01 (27 - 61)

0.297

Unknown

1

1

0

nb_mcs

90

51.14 ± 8.25 (22 - 70)

50.86 ± 8.31 (22 - 68)

51.48 ± 8.27 (35 - 70)

0.724

Unknown

1

1

0

1Mean ± SD (Range)

2Two Sample t-test

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

sets

(Intercept)

18.7

0.384

17.9, 19.4

group

control

—

—

—

treatment

1.21

0.579

0.073, 2.34

0.039

time_point

1st

—

—

—

2nd

-0.088

0.408

-0.888, 0.712

0.829

group * time_point

treatment * 2nd

0.023

0.587

-1.13, 1.17

0.969

Pseudo R square

0.047

setv

(Intercept)

10.9

0.276

10.3, 11.4

group

control

—

—

—

treatment

0.562

0.417

-0.255, 1.38

0.180

time_point

1st

—

—

—

2nd

0.330

0.263

-0.186, 0.846

0.215

group * time_point

treatment * 2nd

-0.177

0.378

-0.918, 0.563

0.640

Pseudo R square

0.020

maks

(Intercept)

44.4

0.556

43.3, 45.5

group

control

—

—

—

treatment

1.07

0.834

-0.565, 2.71

0.202

time_point

1st

—

—

—

2nd

0.029

0.501

-0.952, 1.01

0.953

group * time_point

treatment * 2nd

-0.090

0.717

-1.50, 1.32

0.900

Pseudo R square

0.017

ibs

(Intercept)

15.3

0.358

14.6, 16.0

group

control

—

—

—

treatment

0.367

0.540

-0.691, 1.42

0.498

time_point

1st

—

—

—

2nd

0.257

0.307

-0.345, 0.859

0.406

group * time_point

treatment * 2nd

0.340

0.440

-0.523, 1.20

0.443

Pseudo R square

0.018

ers_e

(Intercept)

12.0

0.262

11.5, 12.5

group

control

—

—

—

treatment

0.295

0.395

-0.481, 1.07

0.458

time_point

1st

—

—

—

2nd

-0.458

0.204

-0.857, -0.059

0.028

group * time_point

treatment * 2nd

0.657

0.291

0.086, 1.23

0.028

Pseudo R square

0.031

ers_r

(Intercept)

11.0

0.245

10.5, 11.5

group

control

—

—

—

treatment

0.495

0.369

-0.228, 1.22

0.183

time_point

1st

—

—

—

2nd

0.030

0.263

-0.486, 0.546

0.910

group * time_point

treatment * 2nd

0.035

0.379

-0.707, 0.777

0.927

Pseudo R square

0.021

pss_pa

(Intercept)

43.8

0.859

42.1, 45.5

group

control

—

—

—

treatment

1.70

1.295

-0.836, 4.24

0.192

time_point

1st

—

—

—

2nd

-1.24

0.833

-2.87, 0.397

0.143

group * time_point

treatment * 2nd

0.182

1.196

-2.16, 2.53

0.880

Pseudo R square

0.028

pss_ps

(Intercept)

25.8

1.060

23.7, 27.8

group

control

—

—

—

treatment

-1.26

1.599

-4.40, 1.87

0.430

time_point

1st

—

—

—

2nd

1.53

1.131

-0.685, 3.75

0.180

group * time_point

treatment * 2nd

-1.52

1.626

-4.71, 1.67

0.353

Pseudo R square

0.020

pss

(Intercept)

43.7

1.616

40.5, 46.9

group

control

—

—

—

treatment

-1.73

2.437

-6.51, 3.05

0.479

time_point

1st

—

—

—

2nd

3.18

1.646

-0.047, 6.40

0.057

group * time_point

treatment * 2nd

-2.09

2.363

-6.73, 2.54

0.379

Pseudo R square

0.022

rki_responsible

(Intercept)

20.5

0.614

19.2, 21.7

group

control

—

—

—

treatment

1.25

0.925

-0.565, 3.06

0.180

time_point

1st

—

—

—

2nd

0.123

0.605

-1.06, 1.31

0.840

group * time_point

treatment * 2nd

-0.580

0.869

-2.28, 1.12

0.507

Pseudo R square

0.014

rki_nonlinear

(Intercept)

12.9

0.443

12.1, 13.8

group

control

—

—

—

treatment

0.753

0.668

-0.556, 2.06

0.262

time_point

1st

—

—

—

2nd

-0.234

0.457

-1.13, 0.662

0.610

group * time_point

treatment * 2nd

0.445

0.657

-0.842, 1.73

0.500

Pseudo R square

0.023

rki_peer

(Intercept)

20.1

0.412

19.3, 20.9

group

control

—

—

—

treatment

0.297

0.621

-0.921, 1.51

0.634

time_point

1st

—

—

—

2nd

0.101

0.389

-0.662, 0.864

0.796

group * time_point

treatment * 2nd

0.117

0.558

-0.978, 1.21

0.835

Pseudo R square

0.004

rki_expect

(Intercept)

4.39

0.150

4.10, 4.69

group

control

—

—

—

treatment

0.533

0.226

0.089, 0.977

0.020

time_point

1st

—

—

—

2nd

0.208

0.197

-0.178, 0.594

0.295

group * time_point

treatment * 2nd

-0.015

0.285

-0.573, 0.543

0.959

Pseudo R square

0.067

rki

(Intercept)

57.8

1.145

55.6, 60.1

group

control

—

—

—

treatment

2.83

1.727

-0.553, 6.22

0.104

time_point

1st

—

—

—

2nd

0.003

0.938

-1.83, 1.84

0.997

group * time_point

treatment * 2nd

0.271

1.343

-2.36, 2.90

0.841

Pseudo R square

0.032

raq_possible

(Intercept)

15.3

0.316

14.7, 15.9

group

control

—

—

—

treatment

0.211

0.477

-0.723, 1.15

0.659

time_point

1st

—

—

—

2nd

-0.200

0.318

-0.823, 0.422

0.531

group * time_point

treatment * 2nd

0.612

0.456

-0.281, 1.51

0.184

Pseudo R square

0.016

raq_difficulty

(Intercept)

12.2

0.255

11.7, 12.7

group

control

—

—

—

treatment

-0.041

0.385

-0.795, 0.713

0.916

time_point

1st

—

—

—

2nd

0.061

0.234

-0.397, 0.519

0.794

group * time_point

treatment * 2nd

0.144

0.335

-0.513, 0.801

0.669

Pseudo R square

0.002

raq

(Intercept)

27.5

0.551

26.5, 28.6

group

control

—

—

—

treatment

0.171

0.831

-1.46, 1.80

0.838

time_point

1st

—

—

—

2nd

-0.153

0.473

-1.08, 0.773

0.747

group * time_point

treatment * 2nd

0.766

0.677

-0.561, 2.09

0.262

Pseudo R square

0.007

who

(Intercept)

14.8

0.637

13.5, 16.0

group

control

—

—

—

treatment

0.260

0.960

-1.62, 2.14

0.787

time_point

1st

—

—

—

2nd

-0.265

0.596

-1.43, 0.904

0.658

group * time_point

treatment * 2nd

0.819

0.855

-0.857, 2.49

0.342

Pseudo R square

0.007

phq

(Intercept)

3.29

0.488

2.34, 4.25

group

control

—

—

—

treatment

0.156

0.737

-1.29, 1.60

0.833

time_point

1st

—

—

—

2nd

0.150

0.375

-0.586, 0.886

0.691

group * time_point

treatment * 2nd

-0.114

0.537

-1.17, 0.939

0.832

Pseudo R square

0.001

gad

(Intercept)

2.90

0.432

2.06, 3.75

group

control

—

—

—

treatment

-0.077

0.652

-1.35, 1.20

0.906

time_point

1st

—

—

—

2nd

0.353

0.418

-0.466, 1.17

0.400

group * time_point

treatment * 2nd

-0.204

0.600

-1.38, 0.972

0.735

Pseudo R square

0.003

nb_pcs

(Intercept)

51.9

1.046

49.9, 54.0

group

control

—

—

—

treatment

-1.72

1.569

-4.80, 1.35

0.275

time_point

1st

—

—

—

2nd

-0.755

0.902

-2.52, 1.01

0.405

group * time_point

treatment * 2nd

1.74

1.291

-0.787, 4.27

0.181

Pseudo R square

0.008

nb_mcs

(Intercept)

50.9

1.143

48.6, 53.1

group

control

—

—

—

treatment

0.622

1.715

-2.74, 3.98

0.717

time_point

1st

—

—

—

2nd

-0.413

1.205

-2.77, 1.95

0.733

group * time_point

treatment * 2nd

0.325

1.731

-3.07, 3.72

0.851

Pseudo R square

0.002

1SE = Standard Error, CI = Confidence Interval

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 18.67 (95% CI [17.91, 19.42], t(152) = 48.59, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 1.21, 95% CI [0.07, 2.34], t(152) = 2.09, p = 0.037; Std. beta = 0.45, 95% CI [0.03, 0.88])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.89, 0.71], t(152) = -0.22, p = 0.828; Std. beta = -0.03, 95% CI [-0.33, 0.27])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.02, 95% CI [-1.13, 1.17], t(152) = 0.04, p = 0.969; Std. beta = 8.66e-03, 95% CI [-0.42, 0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.69) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 10.86 (95% CI [10.32, 11.40], t(152) = 39.31, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.56, 95% CI [-0.25, 1.38], t(152) = 1.35, p = 0.177; Std. beta = 0.29, 95% CI [-0.13, 0.72])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.33, 95% CI [-0.19, 0.85], t(152) = 1.25, p = 0.210; Std. beta = 0.17, 95% CI [-0.10, 0.44])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.18, 95% CI [-0.92, 0.56], t(152) = -0.47, p = 0.639; Std. beta = -0.09, 95% CI [-0.48, 0.29])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.38 (95% CI [43.29, 45.47], t(151) = 79.80, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.07, 95% CI [-0.57, 2.71], t(151) = 1.28, p = 0.200; Std. beta = 0.27, 95% CI [-0.14, 0.69])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.95, 1.01], t(151) = 0.06, p = 0.953; Std. beta = 7.50e-03, 95% CI [-0.24, 0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-1.50, 1.32], t(151) = -0.13, p = 0.900; Std. beta = -0.02, 95% CI [-0.38, 0.34])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.75) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.33 (95% CI [14.63, 16.03], t(152) = 42.85, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.37, 95% CI [-0.69, 1.42], t(152) = 0.68, p = 0.497; Std. beta = 0.15, 95% CI [-0.28, 0.58])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.26, 95% CI [-0.35, 0.86], t(152) = 0.84, p = 0.403; Std. beta = 0.10, 95% CI [-0.14, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.52, 1.20], t(152) = 0.77, p = 0.440; Std. beta = 0.14, 95% CI [-0.21, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.80) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.98 (95% CI [11.47, 12.49], t(152) = 45.69, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-0.48, 1.07], t(152) = 0.74, p = 0.456; Std. beta = 0.17, 95% CI [-0.28, 0.61])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.46, 95% CI [-0.86, -0.06], t(152) = -2.25, p = 0.025; Std. beta = -0.26, 95% CI [-0.49, -0.03])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 0.66, 95% CI [0.09, 1.23], t(152) = 2.25, p = 0.024; Std. beta = 0.38, 95% CI [0.05, 0.70])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 10.98 (95% CI [10.50, 11.46], t(152) = 44.89, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.49, 95% CI [-0.23, 1.22], t(152) = 1.34, p = 0.180; Std. beta = 0.29, 95% CI [-0.14, 0.72])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.49, 0.55], t(152) = 0.11, p = 0.910; Std. beta = 0.02, 95% CI [-0.29, 0.32])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.71, 0.78], t(152) = 0.09, p = 0.926; Std. beta = 0.02, 95% CI [-0.42, 0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 43.82 (95% CI [42.14, 45.51], t(152) = 51.05, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.70, 95% CI [-0.84, 4.24], t(152) = 1.31, p = 0.189; Std. beta = 0.29, 95% CI [-0.14, 0.73])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -1.24, 95% CI [-2.87, 0.40], t(152) = -1.48, p = 0.138; Std. beta = -0.21, 95% CI [-0.50, 0.07])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.18, 95% CI [-2.16, 2.53], t(152) = 0.15, p = 0.879; Std. beta = 0.03, 95% CI [-0.37, 0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 25.76 (95% CI [23.69, 27.84], t(152) = 24.31, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -1.26, 95% CI [-4.40, 1.87], t(152) = -0.79, p = 0.429; Std. beta = -0.17, 95% CI [-0.58, 0.25])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 1.53, 95% CI [-0.68, 3.75], t(152) = 1.35, p = 0.176; Std. beta = 0.20, 95% CI [-0.09, 0.50])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.52, 95% CI [-4.71, 1.67], t(152) = -0.93, p = 0.350; Std. beta = -0.20, 95% CI [-0.62, 0.22])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 43.71 (95% CI [40.54, 46.87], t(152) = 27.05, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -1.73, 95% CI [-6.51, 3.05], t(152) = -0.71, p = 0.478; Std. beta = -0.15, 95% CI [-0.57, 0.26])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 3.18, 95% CI [-0.05, 6.40], t(152) = 1.93, p = 0.053; Std. beta = 0.28, 95% CI [-4.11e-03, 0.56])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -2.09, 95% CI [-6.73, 2.54], t(152) = -0.89, p = 0.376; Std. beta = -0.18, 95% CI [-0.58, 0.22])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.45 (95% CI [19.25, 21.65], t(152) = 33.33, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.25, 95% CI [-0.56, 3.06], t(152) = 1.35, p = 0.177; Std. beta = 0.30, 95% CI [-0.13, 0.72])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.12, 95% CI [-1.06, 1.31], t(152) = 0.20, p = 0.839; Std. beta = 0.03, 95% CI [-0.25, 0.31])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.58, 95% CI [-2.28, 1.12], t(152) = -0.67, p = 0.504; Std. beta = -0.14, 95% CI [-0.54, 0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.92 (95% CI [12.05, 13.79], t(152) = 29.18, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.75, 95% CI [-0.56, 2.06], t(152) = 1.13, p = 0.259; Std. beta = 0.24, 95% CI [-0.18, 0.67])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.23, 95% CI [-1.13, 0.66], t(152) = -0.51, p = 0.609; Std. beta = -0.08, 95% CI [-0.37, 0.21])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.45, 95% CI [-0.84, 1.73], t(152) = 0.68, p = 0.498; Std. beta = 0.14, 95% CI [-0.27, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.69) and the part related to the fixed effects alone (marginal R2) is of 4.40e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.08 (95% CI [19.27, 20.89], t(152) = 48.74, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.30, 95% CI [-0.92, 1.51], t(152) = 0.48, p = 0.633; Std. beta = 0.11, 95% CI [-0.33, 0.55])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.66, 0.86], t(152) = 0.26, p = 0.795; Std. beta = 0.04, 95% CI [-0.24, 0.31])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.98, 1.21], t(152) = 0.21, p = 0.834; Std. beta = 0.04, 95% CI [-0.35, 0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.40) and the part related to the fixed effects alone (marginal R2) is of 0.07. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.39 (95% CI [4.10, 4.69], t(152) = 29.25, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.53, 95% CI [0.09, 0.98], t(152) = 2.35, p = 0.019; Std. beta = 0.49, 95% CI [0.08, 0.90])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.18, 0.59], t(152) = 1.06, p = 0.291; Std. beta = 0.19, 95% CI [-0.16, 0.55])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.01, 95% CI [-0.57, 0.54], t(152) = -0.05, p = 0.959; Std. beta = -0.01, 95% CI [-0.53, 0.50])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.77) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 57.84 (95% CI [55.60, 60.09], t(152) = 50.52, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 2.83, 95% CI [-0.55, 6.22], t(152) = 1.64, p = 0.101; Std. beta = 0.38, 95% CI [-0.07, 0.83])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 3.07e-03, 95% CI [-1.83, 1.84], t(152) = 3.28e-03, p = 0.997; Std. beta = 4.11e-04, 95% CI [-0.25, 0.25])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.27, 95% CI [-2.36, 2.90], t(152) = 0.20, p = 0.840; Std. beta = 0.04, 95% CI [-0.32, 0.39])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.65) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.31 (95% CI [14.69, 15.93], t(152) = 48.45, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.21, 95% CI [-0.72, 1.15], t(152) = 0.44, p = 0.658; Std. beta = 0.10, 95% CI [-0.34, 0.53])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.20, 95% CI [-0.82, 0.42], t(152) = -0.63, p = 0.528; Std. beta = -0.09, 95% CI [-0.38, 0.20])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.61, 95% CI [-0.28, 1.51], t(152) = 1.34, p = 0.179; Std. beta = 0.28, 95% CI [-0.13, 0.70])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.71) and the part related to the fixed effects alone (marginal R2) is of 1.66e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.22 (95% CI [11.72, 12.72], t(152) = 47.89, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.04, 95% CI [-0.79, 0.71], t(152) = -0.11, p = 0.916; Std. beta = -0.02, 95% CI [-0.46, 0.42])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.06, 95% CI [-0.40, 0.52], t(152) = 0.26, p = 0.794; Std. beta = 0.04, 95% CI [-0.23, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.51, 0.80], t(152) = 0.43, p = 0.667; Std. beta = 0.08, 95% CI [-0.30, 0.47])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.75) and the part related to the fixed effects alone (marginal R2) is of 7.13e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 27.53 (95% CI [26.45, 28.61], t(152) = 49.99, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.17, 95% CI [-1.46, 1.80], t(152) = 0.21, p = 0.837; Std. beta = 0.05, 95% CI [-0.40, 0.49])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-1.08, 0.77], t(152) = -0.32, p = 0.746; Std. beta = -0.04, 95% CI [-0.29, 0.21])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.77, 95% CI [-0.56, 2.09], t(152) = 1.13, p = 0.258; Std. beta = 0.21, 95% CI [-0.15, 0.57])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.69) and the part related to the fixed effects alone (marginal R2) is of 6.71e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.76 (95% CI [13.52, 16.01], t(152) = 23.19, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.26, 95% CI [-1.62, 2.14], t(152) = 0.27, p = 0.786; Std. beta = 0.06, 95% CI [-0.36, 0.48])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.26, 95% CI [-1.43, 0.90], t(152) = -0.44, p = 0.657; Std. beta = -0.06, 95% CI [-0.32, 0.20])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.82, 95% CI [-0.86, 2.49], t(152) = 0.96, p = 0.338; Std. beta = 0.18, 95% CI [-0.19, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.80) and the part related to the fixed effects alone (marginal R2) is of 5.10e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.29 (95% CI [2.34, 4.25], t(152) = 6.75, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.16, 95% CI [-1.29, 1.60], t(152) = 0.21, p = 0.832; Std. beta = 0.04, 95% CI [-0.37, 0.46])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.15, 95% CI [-0.59, 0.89], t(152) = 0.40, p = 0.690; Std. beta = 0.04, 95% CI [-0.17, 0.25])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.11, 95% CI [-1.17, 0.94], t(152) = -0.21, p = 0.831; Std. beta = -0.03, 95% CI [-0.34, 0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 2.57e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.90 (95% CI [2.06, 3.75], t(152) = 6.72, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-1.35, 1.20], t(152) = -0.12, p = 0.906; Std. beta = -0.02, 95% CI [-0.44, 0.39])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.35, 95% CI [-0.47, 1.17], t(152) = 0.85, p = 0.398; Std. beta = 0.11, 95% CI [-0.15, 0.38])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.20, 95% CI [-1.38, 0.97], t(152) = -0.34, p = 0.734; Std. beta = -0.07, 95% CI [-0.45, 0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.74) and the part related to the fixed effects alone (marginal R2) is of 7.67e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.92 (95% CI [49.87, 53.97], t(151) = 49.63, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -1.72, 95% CI [-4.80, 1.35], t(151) = -1.10, p = 0.272; Std. beta = -0.23, 95% CI [-0.65, 0.18])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.76, 95% CI [-2.52, 1.01], t(151) = -0.84, p = 0.402; Std. beta = -0.10, 95% CI [-0.34, 0.14])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.74, 95% CI [-0.79, 4.27], t(151) = 1.35, p = 0.177; Std. beta = 0.24, 95% CI [-0.11, 0.58])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 2.50e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.86 (95% CI [48.62, 53.10], t(151) = 44.49, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.62, 95% CI [-2.74, 3.98], t(151) = 0.36, p = 0.717; Std. beta = 0.08, 95% CI [-0.34, 0.49])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.41, 95% CI [-2.77, 1.95], t(151) = -0.34, p = 0.732; Std. beta = -0.05, 95% CI [-0.34, 0.24])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.33, 95% CI [-3.07, 3.72], t(151) = 0.19, p = 0.851; Std. beta = 0.04, 95% CI [-0.38, 0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

sets

null

3

745.042

754.230

-369.521

739.042

sets

random

6

745.766

764.142

-366.883

733.766

5.276

3

0.153

setv

null

3

627.145

636.333

-310.573

621.145

setv

random

6

629.490

647.866

-308.745

617.490

3.655

3

0.301

maks

null

3

832.017

841.186

-413.008

826.017

maks

random

6

836.241

854.579

-412.121

824.241

1.776

3

0.620

ibs

null

3

698.262

707.450

-346.131

692.262

ibs

random

6

698.913

717.288

-343.456

686.913

5.349

3

0.148

ers_e

null

3

590.252

599.440

-292.126

584.252

ers_e

random

6

588.497

606.873

-288.249

576.497

7.755

3

0.051

ers_r

null

3

600.914

610.102

-297.457

594.914

ers_r

random

6

604.515

622.890

-296.257

592.515

2.400

3

0.494

pss_pa

null

3

989.469

998.656

-491.734

983.469

pss_pa

random

6

989.831

1,008.207

-488.916

977.831

5.637

3

0.131

pss_ps

null

3

1,064.361

1,073.549

-529.180

1,058.361

pss_ps

random

6

1,066.954

1,085.330

-527.477

1,054.954

3.407

3

0.333

pss

null

3

1,194.357

1,203.545

-594.178

1,188.357

pss

random

6

1,195.047

1,213.423

-591.523

1,183.047

5.310

3

0.150

rki_responsible

null

3

881.490

890.677

-437.745

875.490

rki_responsible

random

6

885.493

903.869

-436.747

873.493

1.996

3

0.573

rki_nonlinear

null

3

784.312

793.500

-389.156

778.312

rki_nonlinear

random

6

787.537

805.913

-387.769

775.537

2.774

3

0.428

rki_peer

null

3

749.498

758.686

-371.749

743.498

rki_peer

random

6

754.748

773.124

-371.374

742.748

0.750

3

0.861

rki_expect

null

3

472.847

482.034

-233.423

466.847

rki_expect

random

6

469.245

487.620

-228.622

457.245

9.602

3

0.022

rki

null

3

1,058.101

1,067.289

-526.050

1,052.101

rki

random

6

1,060.790

1,079.166

-524.395

1,048.790

3.311

3

0.346

raq_possible

null

3

675.090

684.277

-334.545

669.090

raq_possible

random

6

677.986

696.361

-332.993

665.986

3.104

3

0.376

raq_difficulty

null

3

594.565

603.752

-294.282

588.565

raq_difficulty

random

6

599.743

618.119

-293.871

587.743

0.822

3

0.844

raq

null

3

831.252

840.440

-412.626

825.252

raq

random

6

835.139

853.514

-411.569

823.139

2.113

3

0.549

who

null

3

886.696

895.884

-440.348

880.696

who

random

6

891.223

909.599

-439.612

879.223

1.473

3

0.689

phq

null

3

777.993

787.181

-385.997

771.993

phq

random

6

783.793

802.169

-385.897

771.793

0.200

3

0.978

gad

null

3

767.378

776.566

-380.689

761.378

gad

random

6

772.475

790.851

-380.238

760.475

0.903

3

0.825

nb_pcs

null

3

1,025.885

1,035.054

-509.943

1,019.885

nb_pcs

random

6

1,029.511

1,047.848

-508.755

1,017.511

2.374

3

0.498

nb_mcs

null

3

1,074.650

1,083.819

-534.325

1,068.650

nb_mcs

random

6

1,080.304

1,098.641

-534.152

1,068.304

0.346

3

0.951

Post hoc analysis text

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

sets

1st

51

18.67 ± 2.74

40

19.88 ± 2.74

0.039

-0.693

sets

2nd

34

18.58 ± 2.58

0.051

33

19.81 ± 2.66

0.038

0.057

-0.707

setv

1st

51

10.86 ± 1.97

40

11.42 ± 1.97

0.180

-0.504

setv

2nd

34

11.19 ± 1.82

-0.296

33

11.58 ± 1.89

-0.137

0.398

-0.345

maks

1st

50

44.38 ± 3.93

40

45.45 ± 3.93

0.202

-0.506

maks

2nd

34

44.41 ± 3.61

-0.014

33

45.39 ± 3.76

0.029

0.278

-0.464

ibs

1st

51

15.33 ± 2.56

40

15.70 ± 2.56

0.498

-0.283

ibs

2nd

34

15.59 ± 2.31

-0.199

33

16.30 ± 2.43

-0.461

0.225

-0.546

ers_e

1st

51

11.98 ± 1.87

40

12.27 ± 1.87

0.458

-0.345

ers_e

2nd

34

11.52 ± 1.67

0.536

33

12.47 ± 1.77

-0.233

0.025

-1.114

ers_r

1st

51

10.98 ± 1.75

40

11.48 ± 1.75

0.183

-0.440

ers_r

2nd

34

11.01 ± 1.65

-0.026

33

11.54 ± 1.70

-0.057

0.197

-0.471

pss_pa

1st

51

43.82 ± 6.13

40

45.52 ± 6.13

0.191

-0.481

pss_pa

2nd

34

42.59 ± 5.67

0.350

33

44.47 ± 5.89

0.298

0.185

-0.533

pss_ps

1st

51

25.76 ± 7.57

40

24.50 ± 7.57

0.430

0.262

pss_ps

2nd

34

27.30 ± 7.13

-0.317

33

24.51 ± 7.34

-0.003

0.118

0.576

pss

1st

51

43.71 ± 11.54

40

41.98 ± 11.54

0.479

0.247

pss

2nd

34

46.88 ± 10.76

-0.454

33

43.06 ± 11.14

-0.155

0.155

0.546

rki_responsible

1st

51

20.45 ± 4.38

40

21.70 ± 4.38

0.180

-0.486

rki_responsible

2nd

34

20.57 ± 4.06

-0.048

33

21.24 ± 4.22

0.178

0.510

-0.260

rki_nonlinear

1st

51

12.92 ± 3.16

40

13.67 ± 3.16

0.262

-0.387

rki_nonlinear

2nd

34

12.69 ± 2.96

0.120

33

13.89 ± 3.06

-0.108

0.105

-0.615

rki_peer

1st

51

20.08 ± 2.94

40

20.38 ± 2.94

0.634

-0.180

rki_peer

2nd

34

20.18 ± 2.71

-0.061

33

20.59 ± 2.82

-0.132

0.542

-0.251

rki_expect

1st

51

4.39 ± 1.07

40

4.93 ± 1.07

0.020

-0.620

rki_expect

2nd

34

4.60 ± 1.05

-0.242

33

5.12 ± 1.06

-0.225

0.047

-0.603

rki

1st

51

57.84 ± 8.18

40

60.67 ± 8.18

0.104

-0.718

rki

2nd

34

57.85 ± 7.34

-0.001

33

60.95 ± 7.75

-0.070

0.095

-0.787

raq_possible

1st

51

15.31 ± 2.26

40

15.53 ± 2.26

0.658

-0.156

raq_possible

2nd

34

15.11 ± 2.10

0.148

33

15.94 ± 2.18

-0.305

0.117

-0.610

raq_difficulty

1st

51

12.22 ± 1.82

40

12.18 ± 1.82

0.916

0.041

raq_difficulty

2nd

34

12.28 ± 1.67

-0.062

33

12.38 ± 1.74

-0.208

0.804

-0.105

raq

1st

51

27.53 ± 3.93

40

27.70 ± 3.93

0.838

-0.086

raq

2nd

34

27.38 ± 3.56

0.077

33

28.31 ± 3.74

-0.308

0.296

-0.470

who

1st

51

14.76 ± 4.55

40

15.03 ± 4.55

0.787

-0.103

who

2nd

34

14.50 ± 4.17

0.105

33

15.58 ± 4.35

-0.220

0.303

-0.428

phq

1st

51

3.29 ± 3.49

40

3.45 ± 3.49

0.833

-0.099

phq

2nd

34

3.44 ± 3.10

-0.095

33

3.49 ± 3.29

-0.023

0.958

-0.026

gad

1st

51

2.90 ± 3.09

40

2.83 ± 3.09

0.906

0.043

gad

2nd

34

3.26 ± 2.85

-0.199

33

2.97 ± 2.96

-0.085

0.694

0.158

nb_pcs

1st

50

51.92 ± 7.40

40

50.20 ± 7.40

0.275

0.453

nb_pcs

2nd

34

51.17 ± 6.73

0.199

33

51.19 ± 7.04

-0.260

0.990

-0.006

nb_mcs

1st

50

50.86 ± 8.08

40

51.48 ± 8.08

0.717

-0.121

nb_mcs

2nd

34

50.45 ± 7.63

0.080

33

51.40 ± 7.84

0.017

0.617

-0.184

Between group

sets

1st

t(119.88) = 2.09, p = 0.039, Cohen d = -0.69, 95% CI (0.06 to 2.36)

2st

t(140.72) = 1.92, p = 0.057, Cohen d = -0.71, 95% CI (-0.04 to 2.50)

setv

1st

t(112.88) = 1.35, p = 0.180, Cohen d = -0.50, 95% CI (-0.26 to 1.39)

2st

t(134.26) = 0.85, p = 0.398, Cohen d = -0.34, 95% CI (-0.51 to 1.28)

maks

1st

t(109.34) = 1.28, p = 0.202, Cohen d = -0.51, 95% CI (-0.58 to 2.72)

2st

t(129.60) = 1.09, p = 0.278, Cohen d = -0.46, 95% CI (-0.80 to 2.76)

ibs

1st

t(107.73) = 0.68, p = 0.498, Cohen d = -0.28, 95% CI (-0.70 to 1.44)

2st

t(127.97) = 1.22, p = 0.225, Cohen d = -0.55, 95% CI (-0.44 to 1.85)

ers_e

1st

t(103.91) = 0.74, p = 0.458, Cohen d = -0.34, 95% CI (-0.49 to 1.08)

2st

t(122.24) = 2.27, p = 0.025, Cohen d = -1.11, 95% CI (0.12 to 1.78)

ers_r

1st

t(120.86) = 1.34, p = 0.183, Cohen d = -0.44, 95% CI (-0.24 to 1.22)

2st

t(141.47) = 1.30, p = 0.197, Cohen d = -0.47, 95% CI (-0.28 to 1.34)

pss_pa

1st

t(113.93) = 1.31, p = 0.191, Cohen d = -0.48, 95% CI (-0.86 to 4.27)

2st

t(135.37) = 1.33, p = 0.185, Cohen d = -0.53, 95% CI (-0.91 to 4.68)

pss_ps

1st

t(120.23) = -0.79, p = 0.430, Cohen d = 0.26, 95% CI (-4.43 to 1.90)

2st

t(140.99) = -1.57, p = 0.118, Cohen d = 0.58, 95% CI (-6.28 to 0.71)

pss

1st

t(116.94) = -0.71, p = 0.479, Cohen d = 0.25, 95% CI (-6.56 to 3.09)

2st

t(138.27) = -1.43, p = 0.155, Cohen d = 0.55, 95% CI (-9.12 to 1.47)

rki_responsible

1st

t(114.89) = 1.35, p = 0.180, Cohen d = -0.49, 95% CI (-0.58 to 3.08)

2st

t(136.34) = 0.66, p = 0.510, Cohen d = -0.26, 95% CI (-1.33 to 2.67)

rki_nonlinear

1st

t(117.83) = 1.13, p = 0.262, Cohen d = -0.39, 95% CI (-0.57 to 2.08)

2st

t(139.05) = 1.63, p = 0.105, Cohen d = -0.62, 95% CI (-0.26 to 2.65)

rki_peer

1st

t(112.41) = 0.48, p = 0.634, Cohen d = -0.18, 95% CI (-0.93 to 1.53)

2st

t(133.75) = 0.61, p = 0.542, Cohen d = -0.25, 95% CI (-0.92 to 1.75)

rki_expect

1st

t(139.25) = 2.35, p = 0.020, Cohen d = -0.62, 95% CI (0.09 to 0.98)

2st

t(150.63) = 2.00, p = 0.047, Cohen d = -0.60, 95% CI (0.01 to 1.03)

rki

1st

t(105.82) = 1.64, p = 0.104, Cohen d = -0.72, 95% CI (-0.59 to 6.26)

2st

t(125.22) = 1.68, p = 0.095, Cohen d = -0.79, 95% CI (-0.55 to 6.75)

raq_possible

1st

t(116.05) = 0.44, p = 0.658, Cohen d = -0.16, 95% CI (-0.73 to 1.16)

2st

t(137.45) = 1.58, p = 0.117, Cohen d = -0.61, 95% CI (-0.21 to 1.86)

raq_difficulty

1st

t(110.80) = -0.11, p = 0.916, Cohen d = 0.04, 95% CI (-0.80 to 0.72)

2st

t(131.90) = 0.25, p = 0.804, Cohen d = -0.10, 95% CI (-0.72 to 0.93)

raq

1st

t(107.72) = 0.21, p = 0.838, Cohen d = -0.09, 95% CI (-1.48 to 1.82)

2st

t(127.96) = 1.05, p = 0.296, Cohen d = -0.47, 95% CI (-0.83 to 2.70)

who

1st

t(111.92) = 0.27, p = 0.787, Cohen d = -0.10, 95% CI (-1.64 to 2.16)

2st

t(133.20) = 1.03, p = 0.303, Cohen d = -0.43, 95% CI (-0.98 to 3.14)

phq

1st

t(103.58) = 0.21, p = 0.833, Cohen d = -0.10, 95% CI (-1.30 to 1.62)

2st

t(121.69) = 0.05, p = 0.958, Cohen d = -0.03, 95% CI (-1.51 to 1.59)

gad

1st

t(113.72) = -0.12, p = 0.906, Cohen d = 0.04, 95% CI (-1.37 to 1.21)

2st

t(135.16) = -0.39, p = 0.694, Cohen d = 0.16, 95% CI (-1.69 to 1.13)

nb_pcs

1st

t(107.30) = -1.10, p = 0.275, Cohen d = 0.45, 95% CI (-4.83 to 1.39)

2st

t(126.98) = 0.01, p = 0.990, Cohen d = -0.01, 95% CI (-3.31 to 3.35)

nb_mcs

1st

t(118.92) = 0.36, p = 0.717, Cohen d = -0.12, 95% CI (-2.77 to 4.02)

2st

t(139.11) = 0.50, p = 0.617, Cohen d = -0.18, 95% CI (-2.79 to 4.68)

Within treatment group

sets

1st vs 2st

t(69.96) = -0.16, p = 0.877, Cohen d = 0.04, 95% CI (-0.91 to 0.78)

setv

1st vs 2st

t(68.87) = 0.56, p = 0.576, Cohen d = -0.14, 95% CI (-0.39 to 0.69)

maks

1st vs 2st

t(68.46) = -0.12, p = 0.906, Cohen d = 0.03, 95% CI (-1.09 to 0.97)

ibs

1st vs 2st

t(68.07) = 1.89, p = 0.063, Cohen d = -0.46, 95% CI (-0.03 to 1.23)

ers_e

1st vs 2st

t(67.47) = 0.95, p = 0.343, Cohen d = -0.23, 95% CI (-0.22 to 0.62)

ers_r

1st vs 2st

t(70.11) = 0.24, p = 0.813, Cohen d = -0.06, 95% CI (-0.48 to 0.61)

pss_pa

1st vs 2st

t(69.04) = -1.23, p = 0.224, Cohen d = 0.30, 95% CI (-2.77 to 0.66)

pss_ps

1st vs 2st

t(70.01) = 0.01, p = 0.991, Cohen d = -0.00, 95% CI (-2.32 to 2.35)

pss

1st vs 2st

t(69.50) = 0.64, p = 0.525, Cohen d = -0.15, 95% CI (-2.30 to 4.47)

rki_responsible

1st vs 2st

t(69.18) = -0.73, p = 0.466, Cohen d = 0.18, 95% CI (-1.70 to 0.79)

rki_nonlinear

1st vs 2st

t(69.64) = 0.45, p = 0.656, Cohen d = -0.11, 95% CI (-0.73 to 1.15)

rki_peer

1st vs 2st

t(68.80) = 0.54, p = 0.588, Cohen d = -0.13, 95% CI (-0.58 to 1.02)

rki_expect

1st vs 2st

t(73.20) = 0.94, p = 0.351, Cohen d = -0.22, 95% CI (-0.22 to 0.60)

rki

1st vs 2st

t(67.77) = 0.29, p = 0.776, Cohen d = -0.07, 95% CI (-1.65 to 2.19)

raq_possible

1st vs 2st

t(69.36) = 1.26, p = 0.213, Cohen d = -0.31, 95% CI (-0.24 to 1.07)

raq_difficulty

1st vs 2st

t(68.55) = 0.85, p = 0.397, Cohen d = -0.21, 95% CI (-0.27 to 0.69)

raq

1st vs 2st

t(68.07) = 1.26, p = 0.211, Cohen d = -0.31, 95% CI (-0.36 to 1.58)

who

1st vs 2st

t(68.73) = 0.90, p = 0.370, Cohen d = -0.22, 95% CI (-0.67 to 1.78)

phq

1st vs 2st

t(67.42) = 0.09, p = 0.927, Cohen d = -0.02, 95% CI (-0.73 to 0.80)

gad

1st vs 2st

t(69.00) = 0.35, p = 0.729, Cohen d = -0.08, 95% CI (-0.71 to 1.01)

nb_pcs

1st vs 2st

t(68.15) = 1.07, p = 0.289, Cohen d = -0.26, 95% CI (-0.86 to 2.84)

nb_mcs

1st vs 2st

t(69.93) = -0.07, p = 0.944, Cohen d = 0.02, 95% CI (-2.57 to 2.39)

Within control group

sets

1st vs 2st

t(74.28) = -0.22, p = 0.829, Cohen d = 0.05, 95% CI (-0.90 to 0.73)

setv

1st vs 2st

t(72.29) = 1.25, p = 0.215, Cohen d = -0.30, 95% CI (-0.20 to 0.86)

maks

1st vs 2st

t(71.27) = 0.06, p = 0.953, Cohen d = -0.01, 95% CI (-0.97 to 1.03)

ibs

1st vs 2st

t(70.80) = 0.83, p = 0.407, Cohen d = -0.20, 95% CI (-0.36 to 0.87)

ers_e

1st vs 2st

t(69.68) = -2.25, p = 0.028, Cohen d = 0.54, 95% CI (-0.86 to -0.05)

ers_r

1st vs 2st

t(74.56) = 0.11, p = 0.911, Cohen d = -0.03, 95% CI (-0.50 to 0.56)

pss_pa

1st vs 2st

t(72.59) = -1.48, p = 0.143, Cohen d = 0.35, 95% CI (-2.90 to 0.43)

pss_ps

1st vs 2st

t(74.38) = 1.35, p = 0.181, Cohen d = -0.32, 95% CI (-0.73 to 3.79)

pss

1st vs 2st

t(73.45) = 1.93, p = 0.058, Cohen d = -0.45, 95% CI (-0.11 to 6.47)

rki_responsible

1st vs 2st

t(72.86) = 0.20, p = 0.840, Cohen d = -0.05, 95% CI (-1.09 to 1.33)

rki_nonlinear

1st vs 2st

t(73.70) = -0.51, p = 0.611, Cohen d = 0.12, 95% CI (-1.15 to 0.68)

rki_peer

1st vs 2st

t(72.16) = 0.26, p = 0.796, Cohen d = -0.06, 95% CI (-0.68 to 0.88)

rki_expect

1st vs 2st

t(79.95) = 1.05, p = 0.296, Cohen d = -0.24, 95% CI (-0.19 to 0.60)

rki

1st vs 2st

t(70.24) = 0.00, p = 0.997, Cohen d = -0.00, 95% CI (-1.87 to 1.88)

raq_possible

1st vs 2st

t(73.19) = -0.63, p = 0.531, Cohen d = 0.15, 95% CI (-0.83 to 0.43)

raq_difficulty

1st vs 2st

t(71.69) = 0.26, p = 0.795, Cohen d = -0.06, 95% CI (-0.41 to 0.53)

raq

1st vs 2st

t(70.80) = -0.32, p = 0.748, Cohen d = 0.08, 95% CI (-1.10 to 0.79)

who

1st vs 2st

t(72.02) = -0.44, p = 0.659, Cohen d = 0.10, 95% CI (-1.46 to 0.93)

phq

1st vs 2st

t(69.58) = 0.40, p = 0.691, Cohen d = -0.10, 95% CI (-0.60 to 0.90)

gad

1st vs 2st

t(72.53) = 0.84, p = 0.402, Cohen d = -0.20, 95% CI (-0.48 to 1.19)

nb_pcs

1st vs 2st

t(70.70) = -0.84, p = 0.406, Cohen d = 0.20, 95% CI (-2.56 to 1.05)

nb_mcs

1st vs 2st

t(73.86) = -0.34, p = 0.734, Cohen d = 0.08, 95% CI (-2.82 to 2.00)

Plot